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2nd Workshop on Ethical Artificial Intelligence: methods and Applications (EAI) 23

2nd Workshop on Ethical Artificial Intelligence: Methods and...

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29th ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD)

作者： Zhao, Chen Chen, Feng Wu, Xintao Chen, Haifeng Zhou, Jiayu Baylor Univ Waco TX 76798 USA Univ Texas Dallas Richardson TX USA Univ Arkansas Fayetteville AR USA NEC Labs Amer Princeton NY USA Michigan State Univ E Lansing MI USA

ISBN: (纸本)9798400701030

Ethical AI has become increasingly important, and it has been attracting attention from academia and industry, due to its increased popularity in real-world applications with fairness concerns. It also places fundamental importance on ethical considerations in determining legitimate and illegitimate uses of AI. Organizations that apply ethical AI have clearly stated well-defined review processes to ensure adherence to legal guidelines. Therefore, the wave of research at the intersection of ethical AI in data mining and machine learning has also influenced other fields of science, including computer vision, natural language processing, reinforcement learning, and social science. Despite these successes, ethical AI still faces many challenges, such as a lack of interpretable and explainable methods for fairness-aware deep learning models, etc. Consequently, there is an urgent need to bring experts and researchers together at prestigious venues to discuss ethical AI, which has been rarely seen in previous KDD conferences. This workshop will provide a premium platform for both research and industry from different backgrounds to exchange ideas on opportunities, challenges, and cutting-edge techniques in ethical AI.

关键词： ethical artificial intelligence fairness-aware machine learning

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A Survey of Event Relation Extraction 11th

A Survey of Event Relation Extraction

引用

11th International conference on Frontier Computing, FC 2021

作者： Xie, QunLi Pan, JunLan Liu, Tao Qian, BeiBei Wang, XianChuan Wang, Xianchao Fuyang Normal University Anhui Fuyang236037 China

ISBN: (纸本)9789811680519

Human beings recognize and understand the real world in units of events. In recent years, events have been used as the basic unit to process unstructured text in the field of natural language processing, but there is often a connection between events and events. Therefore, recognizing the relationship between events and events in unstructured text has become an important task in the field of natural language processing and has attracted more and more researchers’ attention. This paper first introduces the evolution of the method of event temporal relation and causal relation in the extraction research, comparing the advantages and disadvantages and method performance;Then, the event relation extraction model based on deep learning can be divided into strong supervision method and weak supervision method, and the extraction methods of event relation are analyzed, compared and summarized respectively, among them, the method of strong supervision based on deep learning can be further divided into pipeline method and joint learning method, and the method of weak supervision based on deep learning can be divided into semi-supervised learning method, remote learning supervised method and unsupervised learning method. Finally, this paper summarizes the methods of event relation extraction and points out the future research direction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： natural language processing systems

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Speech Mastery Detection Using Advanced natural language processing (NLP) and Automatic Speech Recognition (ASR) Techniques

Speech Mastery Detection Using Advanced Natural Language Pro...

引用

Artificial Intelligence and Signal processing (AISP), INDIA, International conference on

作者： Pratikhya Raut Korikana Anitha Sasubilli Sowmya Kongara Vinnu Department of Electronics and Communication Engineering Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

In today's globalized world, English communication skills are essential for career advancement and cross-cultural collaboration, enhancing access to information and opportunities. Automatic speech recognition, or ASR, is a separate machine-driven method for transcription and decoding spoken language. An ASR system typically uses a microphone to capture a speaker's audio input, analyze it using a model, algorithm, or pattern, and output the results, which are often text messages (Lai, Karat, Yankelovich, 2008). This paper provides a thorough method for utilizing Python-based tools and modules to extract and analyze linguistic characteristics from audio data. The suggested approach turns spoken language into text using voice recognition technology, and then it uses natural language processing (NLP) methods to extract different linguisticmetrics. Word count, sentence count, vocabulary size, average sentence length, average word length, sentiment score, speech pace, frequency of pauses, and average length of pauses are some of these measures. The technique also determines the speaker's speaking style. Keywords: audio analysis, linguistic features, natural language processing, pause detection, speech recognition, sentiment analysis, visualization.

关键词： Training Vocabulary Sentiment analysis Signal processing algorithms Linguistics Feature extraction Size measurement Frequency measurement Speech processing Automatic speech recognition

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Intelligent Algorithm Design and Program Simulation of Lexical Cohesion Method based on Perceptual Pattern Recognition Algorithm in College English Writing 5

Intelligent Algorithm Design and Program Simulation of Lexic...

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5th International conference on Inventive Computation Technologies (ICICT)

作者： Cai, Yi Jilin Univ Sch Foreign Language Educ Changchun 130000 Jilin Peoples R China

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

This paper designs an intelligent algorithm in college English writing based on the lexical cohesion method of perceptual pattern recognition algorithm. First, it analyzes the importance of developing an intelligent English writing training system. The introduction of B/S architecture in the hardware design process makes up for the C/S mode. The main research content is proposed, and then the Entity Framework framework involved in the system is discussed, and the perceptual pattern recognition algorithm is introduced in the lexical cohesion module and software part, and the focus is on sentence segmentation, word segmentation, part-of-speech tagging, and phrase segmentation., spelling proofreading, grammar checking and other natural language processing methods, to deal with the feature extraction and classification of English writing errors.

关键词： Intelligent Algorithm Design Program Simulation Lexical Cohesion Method Perceptual Pattern Recognition College English Writing

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SACR: Self-training with Saliency-Augmented Consistency Regularization for Few-Shot Learners

SACR: Self-training with Saliency-Augmented Consistency Regu...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yanyan Feng Yue Zhou Yun Xue Fenghuan Li Zehong Lin School of Electronics and Information Engineering South China Normal University Foshan China Department of Artiffcial Intelligence School of Informatics Xiamen University Xiamen China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Pre-trained language models have made significant strides in natural language processing tasks, enabling flexible fine-tuning for downstream applications. However, in few-shot learning scenarios, pre-trained models face challenges related to overfitting due to limited training samples, which hinders their ability to capture data diversity and robustly handle input variations. To overcome these limitations, we propose the Saliency-Augmented Consistency Regularization (SACR) framework, a novel self-training strategy designed to improve few-shot learning performance and robustness. SACR consists of three key components: (1) Saliency-guided Data Perturbation, which uses saliency analysis to identify and perturb words that significantly influence model predictions, generating semantically consistent pseudo-samples; (2) Semantic-equivalent Sample Mining, which employs K-means clustering to select semantically similar samples and prevent semantic shift; and (3) Consistency Training, where regularization ensures consistency between the semantics and prediction distributions of the original and perturbed samples. We conducted extensive experiments on 15 public datasets to evaluate our approach. SACR significantly outperforms strong baseline models and demonstrates superior generalization capabilities compared to state-of-the-art methods, achieving an average 1.0% improvement in classification accuracy.

关键词： Training Adaptation models Sensitivity Perturbation methods Semantics Signal processing Data models natural language processing Few shot learning Speech processing

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Larger language Models Don’t Care How You Think: Why Chain-of-Thought Prompting Fails in Subjective Tasks

Larger Language Models Don’t Care How You Think: Why Chain-...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Georgios Chochlakis Niyantha Maruthu Pandiyan Kristina Lerman Shrikanth Narayanan CS and ECE - School of Advanced Computing University of Southern California

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In-Context Learning (ICL) in Large language Models (LLM) has emerged as the dominant technique for performing natural language tasks, as it does not require updating the model parameters with gradient-based methods. ICL promises to "adapt" the LLM to perform the present task at a competitive or state-of-the-art level at a fraction of the computational cost. ICL can be augmented by incorporating the reasoning process to arrive at the final label explicitly in the prompt, a technique called Chain-of-Thought (CoT) prompting. However, recent work has found that ICL relies mostly on the retrieval of task priors and less so on "learning" to perform tasks, especially for complex subjective domains like emotion and morality, where priors ossify posterior predictions. In this work, we examine whether "enabling" reasoning also creates the same behavior in LLMs, wherein the format of CoT retrieves reasoning priors that remain relatively unchanged despite the evidence in the prompt. We find that, surprisingly, CoT indeed suffers from the same posterior collapse as ICL for larger language models. Code is available at https://***/gchochla/cot-priors.

关键词： Codes Large language models Computational modeling natural languages Signal processing Predictive models Benchmark testing Cognition Computational efficiency Speech processing

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LLMs for Generation of Architectural Components: An Exploratory empirical Study in the Serverless World

LLMs for Generation of Architectural Components: An Explorat...

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Working IEEE/IFIP conference on Software Architecture

作者： Shrikara Arun Meghana Tedla Karthik Vaidhyanathan SERC IIIT Hyderabad India

ISBN: (数字)9798331520908

ISBN: (纸本)9798331520915

Recently, the exponential growth in capability and pervasiveness of Large language Models (LLMs) has led to significant work done in the field of code generation. However, this generation has been limited to code snippets. Going one step further, our desideratum is to automatically generate architectural components. This would not only speed up development time, but would also enable us to eventually completely skip the development phase, moving directly from design decisions to deployment. To this end, we conduct an exploratory study on the capability of LLMs to generate architectural components for Functions as a Service (FaaS), commonly known as serverless functions. The small size of their architectural components make this architectural style amenable for generation using current LLMs compared to other styles like monoliths and microservices. We perform the study by systematically selecting open source serverless repositories, masking a serverless function and utilizing state of the art LLMs provided with varying levels of context information about the overall system to generate the masked function. We evaluate correctness through existing tests present in the repositories and use metrics from the Software Engineering (SE) and natural language processing (NLP) domains to evaluate code quality and the degree of similarity between human and LLM generated code respectively. Along with our findings, we also present a discussion on the path forward for using GenAI in architectural component generation.

关键词： Measurement Codes Software architecture Large language models Microservice architectures natural language processing

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frances: A Deep Learning NLP and Text Mining Web Tool to Unlock Historical Digital Collections 18

frances: A Deep Learning NLP and Text Mining Web Tool to Unl...

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IEEE 18th International conference on E-Science (E-Science)

作者： Filgueira, Rosa Univ St Andrews Sch Comp Sci St Andrews Fife Scotland

ISBN: (数字)9781665461245

ISBN: (纸本)9781665461245

This work presents frances, an integrated text mining tool that combines information extraction, knowledge graphs, NLP, deep learning, parallel processing and Semantic Web techniques to unlock the full value of historical digital textual collections, offering new capabilities for researchers to use powerful analysis methods without being distracted by the technology and middleware details. To demonstrate these capabilities, we use the first eight editions of the Encyclopaedia Britannica offered by the National Library of Scotland (NLS) as an example digital collection to mine and analyse. We have developed novel parallel heuristics to extract terms from the original collection (alongside metadata), which provides a mix of unstructured and semi-structured input data, and populated a new knowledge graph with this information. Our natural language processing models enable frances to perform advanced analyses that go significantly beyond simple search using the information stored in the knowledge graph. Furthermore, frances also allows for creating and running complex text mining analyses at scale. Our results show that the novel computational techniques developed within frances provide a vehicle for researchers to formalize and connect findings and insights derived from the analysis of large-scale digital corpora such as the Encyclopaedia Britannica.

关键词： information extraction knowledge graphs deep transfer learning natural language processing text mining web tools semantic web parallel computing digital tools historical digital textual collections

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Design and Implementation of System of the Web Vulnerability Detection Based on Crawler and natural language processing 7

Design and Implementation of System of the Web Vulnerability...

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7th International conference on Information and Network Technologies, ICINT 2022

作者： Ge, Xin Yue, Minnan Information Office University of Shanghai for Science and Technology Shanghai China School of Energy and Power Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9781665482844

ISBN: (纸本)9781665482844

Application code analysis and static rules are the most common methods for Web vulnerability detection, but this process will generate a large amount of contaminated data and network pressure, the false positive rate is high. This study implements a detection system on the basis of the crawler and NLP. The crawler visits page in imitation of a human, we collect the HTTP request and response as dataset, classify the dataset according to parameter characteristic and whether the samples get to interact with a database, then we convert text word vector, reduce the dimension and serialized them, through train dataset by NLP algorithm, finally we obtain a model that can accurately predict Web vulnerabilities. Experimental results show that this method can detect Web vulnerabilities efficiently, greatly reduce invalid attack test parameters, and reduce network pressure. © 2022 IEEE. All rights reserved.

关键词： natural language processing systems

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Unlocking Personality Insights: Assessing Videos with Deep Learning Techniques 3rd

Unlocking Personality Insights: Assessing Videos with Deep L...

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3rd International conference on Signal and Data processing, ICSDP 2023

作者： Athilakshmi, R. Jansi, R. Dutta, Indira Amulya Pavikars, M.M. Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9789819795772

In contemporary times, monitoring criminal behavior and providing mental health therapy requires various activities related to an individual’s character. The development of an automated process for personality assessment through behavior analysis is still under research. Our contribution to this field involves combining deep learning with a regression-based machine learning model through a data pipeline for both audio and video data. We propose a comprehensive classification pipeline that utilizes supervised methods such as LSTM, GRU, and RNN, coupled with Visual Geometry Group (VGG) models for analyzing audio and visual data. The aim is to draw a comparative analysis between these methods to effectively analyze personalities. Our approach yields quality results, achieving an accuracy rate of 88.9% by combining VGG and NN for processing audio-visual data to analyze one’s personality composition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： natural language processing systems

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